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    "# Bioinformatics Algorithm\n",
    "\n",
    "## 过程化考核2: Substitution Matrices\n",
    "\n",
    "> Last Modified Time 2019-09-16 16:30 By ZeFengZhu\n",
    "\n",
    "### 问题描述\n",
    "\n",
    "给出常用的替换矩阵，并说明其具体意义及适用范围\n",
    "\n",
    "### 基础概念\n",
    "\n",
    "> Altschul, Stephen. (2008). Substitution Matrices. 10.1002/9780470015902.a0005265.pub2.\n",
    "\n",
    "\n",
    "#### Score of  Alignment\n",
    "`Alignment`通常有相应`得分`，两者都可以在排列给定序列对的许多可能的方法中选择，并且比较两对序列的比对。更高的分数通常被认为是优越的，对于一对给定的序列，得分最高的对齐方式称为最优对齐。这种比对的分数通常被用作序列相似性的度量。\n",
    "\n",
    "`Alignment`的得分通常是被排列成对应的`核苷酸对`或`氨基酸对`的替换分数之和，以及一个序列中的每一个残基的插入另一个的空字符时的空隙分数。`替换矩阵`仅仅是指定用于对齐所有可能的残基对的分数集合。\n",
    "\n",
    "#### Features of Amino Acid\n",
    "* 具有相似物理/化学性质的氨基酸在进化过程中比具有不同性质的氨基酸更容易相互替代\n",
    "    * 疏水: 异亮氨酸和缬氨酸\n",
    "    * 芳香族: 酪氨酸和苯丙氨酸\n",
    "    * 带电: 精氨酸和赖氨酸(同具有带点侧链+1)\n",
    "    * ...\n",
    "* 具有不同性质的氨基酸相互替代对蛋白有显著影响\n",
    "    * 如果精氨酸突变为谷氨酸，电荷将从+1变为-1，如此剧烈的变化可能会使蛋白质无用\n",
    "    * ...\n",
    "* 20种氨基酸在生物蛋白质中并不是独立、随机、等可能出现\n",
    "    * 某些氨基酸更常见于DNA(具有更多密码子对应): 例如丝氨酸有四个不同的密码子序列，而色氨酸只有一个，因此统计学上更有可能出现丝氨酸\n",
    "    * ...\n",
    "\n",
    "\n",
    "### Match/Mismatch Matrices\n",
    "最早使用及最简单类型的替换分数是一个“匹配/不匹配”矩阵，该矩阵为所有匹配的核苷酸或氨基酸对分配一个实数正分数，为所有不匹配的核苷酸或氨基酸对分配一个固定的负分数。\n",
    "\n",
    "#### 适用范围及缺点\n",
    "* still frequently used for DNA sequence comparison\n",
    "* but it has a major disadvantage in the protein alignment context (忽视了氨基酸替换的特性)\n",
    "\n",
    "---\n",
    "\n",
    "### PAM (Point Accepted Mutation Matrix)\n",
    "> Dayhoff et al. (1978). A model of evolutionary change in proteins. In Atlas of Protein Sequence and Structure, vol. 5, suppl. 3, 345–352. National Biomedical Research Foundation, Silver Spring, MD, 1978.\n",
    "\n",
    "* [Reference Note 1](https://binf.snipcademy.com/lessons/pairwise-alignment/dayhoff-model-accepted-point-mutations \"Link\")\n",
    "\n",
    "#### Konwledge\n",
    "1. Based on the rate of divergence between species\n",
    "2. Developed from Log-odds Substitution Matrices\n",
    "\n",
    "在1978，Dayhoff和她的小组提出了`Accepted Point Mutations` (PAM)。意思是被自然选择所接受的氨基酸残基的突变。\n",
    "\n",
    "为了了解蛋白质进化中所接受的氨基酸，Dayhoff等。在71组密切相关的蛋白质（具有至少85％的同一性）中检查了1572个变化，并观察了所有氨基酸取代。因此，该实验仅基于密切相关物种之间的观察。\n",
    "\n",
    "##### Relative Mutability\n",
    "Dayhoff组计算了每个氨基酸的相对易变性($R_{ij}$)\n",
    "\n",
    "$R_{ij} = \\dfrac{M_{ij}}{f_i}$\n",
    "\n",
    "这里，$M_{ij}$ 是残基$j$在给定进化区间内变为$i$的概率。分母$f_i$表示偶然出现的残基$i$的频率。\n",
    "\n",
    "因此，$R_{ij}$是一个比值比，同时是一个衡量该突变的确实发生而非随机出现的真实性的`indicator`。\n",
    "\n",
    "由此，Dayhoff等人生成了一个表，列出了氨基酸的相对易变性，标准化为整数:\n",
    "\n",
    "|Amino Acid|Mutabilities|\n",
    "-|-\n",
    "|Asn|134|\n",
    "|Ser|120|\n",
    "|Asp|106|\n",
    "|...|...|\n",
    "|Leu|40|\n",
    "|Cys|20|\n",
    "|Trp|18|\n",
    "\n",
    "\n",
    "他们发现`asn, ser, asp`最有可能发生突变，而`leu, cys, trp`最不可能发生突变。\n",
    "\n",
    "Dayhoff还统计了所有蛋白质中各个氨基酸的频率。如果所有氨基酸在蛋白质序列中的概率相等，那么它们的频率都是1.00/20=0.05，但事实并非如如此:\n",
    "\n",
    "Amino Acid|Probabilities\n",
    "-|-\n",
    "Gly|0.089\n",
    "Ala|0.087\n",
    "Leu|0.085\n",
    "...|...\n",
    "Tyr|0.030\n",
    "Met|0.015\n",
    "Trp|0.010\n",
    "\n",
    "##### Mutation rates vary\n",
    "如上表，突变率在氨基酸上是不同的，Dayhoff发现了一种经验化的方法来对氨基酸组成和突变率的变化进行归一化。从Dayhoff的实验中，我们可以看到易变和不易变的残基的特征。\n",
    "* 更容易替换的残基:\n",
    "    * 天冬酰胺是不带电的极性氨基酸组的一部分，它含有六种具有相似性质的其他氨基酸。\n",
    "    * ...\n",
    "* 较不易变残基：\n",
    "    * 具有重要功能\n",
    "        * 如果它有特定的电荷，蛋白质很可能需要这种电荷才能保证功能\n",
    "    * 很难替换\n",
    "        * 色氨酸独特的结构，更换很困难\n",
    "    * ...\n",
    "\n",
    "##### PAM matrices\n",
    "> 将`PAM-1`自乘N次，可以得到`PAM-N`\n",
    "\n",
    "根据这些结果，Dayhoff建立了一个`20×20`的突变概率矩阵，计算每个氨基酸变化的得分。这被称为`PAM1`矩阵，它显示了蛋白质发生1%变化的可能性（每100个氨基酸残基中有1个接受点突变）。`PAM1`矩阵是基于至少有85%同源性的蛋白质序列的`Alignment`生成。\n",
    "\n",
    "在`PAM1`矩阵的生成过程中，一个重要的假设是氨基酸的每一个变化都被假定独立于该位点以前的突变事件。这类假设表明`PAM`是一个`Markov`模型。\n",
    "\n",
    "我们可以由这个假设生成更多的`PAM`矩阵，用于由较长的进化历史周期而被分隔的序列。例如，一个`PAM250`矩阵就是一个`PAM1`矩阵进行矩阵乘法并自乘250次后的结果。这个矩阵适用于氨基酸同源性约为20%的排列，代表了约25亿年的进化。\n",
    "\n",
    "ORIGINAL AMINO ACID\n",
    "\n",
    "NULL | NULL | Ala | Arg | Asn | Asp | Cys | Gln | Glu | Gly | His | Ile | Leu | Lys | Met | Phe | Pro | Ser | Thr | Trp | Tyr | Val\n",
    "- | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - \n",
    "NULL | NULL | A | R | N | D | C | Q | E | G | H | I | L | K | M | F | P | S | T | W | Y | V  \n",
    "Ala | A | 13 | 6 | 9 | 9 | 5 | 8 | 9 | 12 | 6 | 8 | 6 | 7 | 7 | 4 | 11 | 11 | 11 | 2 | 4 | 9  \n",
    "Arg | R | 3 | 17 | 4 | 3 | 2 | 5 | 3 | 2 | 6 | 3 | 2 | 9 | 4 | 1 | 4 | 4 | 3 | 7 | 2 | 2  \n",
    "Asn | N | 4 | 4 | 6 | 7 | 2 | 5 | 6 | 4 | 6 | 3 | 2 | 5 | 3 | 2 | 4 | 5 | 4 | 2 | 3 | 3  \n",
    "Asp | D | 5 | 4 | 8 | 11 | 1 | 7 | 10 | 5 | 6 | 3 | 2 | 5 | 3 | 1 | 4 | 5 | 5 | 1 | 2 | 3  \n",
    "Cys | C | 2 | 1 | 1 | 1 | 52 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 1 | 4 | 2  \n",
    "Gln | Q | 3 | 5 | 5 | 6 | 1 | 10 | 7 | 3 | 7 | 2 | 3 | 5 | 3 | 1 | 4 | 3 | 3 | 1 | 2 | 3  \n",
    "Glu | E | 5 | 4 | 7 | 11 | 1 | 9 | 12 | 5 | 6 | 3 | 2 | 5 | 3 | 1 | 4 | 5 | 5 | 1 | 2 | 3 \n",
    "Gly | G | 12 | 5 | 10 | 10 | 4 | 7 | 9 | 27 | 5 | 5 | 4 | 6 | 5 | 3 | 8 | 11 | 9 | 2 | 3 | 7 \n",
    "His | H | 2 | 5 | 5 | 4 | 2 | 7 | 4 | 2 | 15 | 2 | 2 | 3 | 2 | 2 | 3 | 3 | 2 | 2 | 3 | 2 \n",
    "Ile | I | 3 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 10 | 6 | 2 | 6 | 5 | 2 | 3 | 4 | 1 | 3 | 9 \n",
    "Leu | L | 6 | 4 | 4 | 3 | 2 | 6 | 4 | 3 | 5 | 15 | 34 | 4 | 20 | 13 | 5 | 4 | 6 | 6 | 7 | 13  \n",
    "Lys | K | 6 | 18 | 10 | 8 | 2 | 10 | 8 | 5 | 8 | 5 | 4 | 24 | 9 | 2 | 6 | 8 | 8 | 4 | 3 | 5  \n",
    "Met | M | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 6 | 2 | 1 | 1 | 1 | 1 | 1 | 2  \n",
    "Phe | F | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 3 | 5 | 6 | 1 | 4 | 32 | 1 | 2 | 2 | 4 | 20 | 3 \n",
    "Pro | P | 7 | 5 | 5 | 4 | 3 | 5 | 4 | 5 | 5 | 3 | 3 | 4 | 3 | 2 | 20 | 6 | 5 | 1 | 2 | 4  \n",
    "Ser | S | 9 | 6 | 8 | 7 | 7 | 6 | 7 | 9 | 6 | 5 | 4 | 7 | 5 | 3 | 9 | 10 | 9 | 4 | 4 | 6 \n",
    "Thr | T | 8 | 5 | 6 | 6 | 4 | 5 | 5 | 6 | 4 | 6 | 4 | 6 | 5 | 3 | 6 | 8 | 11 | 2 | 3 | 6 \n",
    "Trp | W | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 55 | 1 | 0 \n",
    "Tyr | Y | 1 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 3 | 2 | 2 | 1 | 2 | 15 | 1 | 2 | 2 | 3 | 31 | 2 \n",
    "Val | V | 7 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 4 | 15 | 10 | 4 | 10 | 5 | 5 | 5 | 72 | 4 | 17 \n",
    "\n",
    "##### Probabilty to Log-Odds Scoring\n",
    "当BLAST对一`Alignment`进行评分时，它不使用上述的概率矩阵。它将矩阵元素转换为整数，并生成一个对数比记分矩阵。\n",
    "\n",
    "$s_{ij}=10*log(\\dfrac{q_{ij}}{p_i})$\n",
    "\n",
    "这里，$s_{ij}$是对齐任意两个残基的分数。如果值很高，这意味着它很好地对齐。$q_{ij}$是一个残基的数量，我会突变到残基$j$。分母是偶然发生的残基突变的概率。\n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "例子:\n",
    "\n",
    "我们取残基M（蛋氨酸）并计算其突变为L（亮氨酸）。这两种氨基酸都有疏水性的侧链，所以它们应该对齐得很好，我们期望得到一个积极的分数。我们将使用`PAM250`突变矩阵:\n",
    "\n",
    "$S_{mm}=10*log(\\dfrac{q_{mm}}{p_m}) \\,\\,\\, \\Rightarrow \\,\\,\\, S_{mm}=10*log(\\dfrac{0.06}{0.015}) \\,\\,\\, \\Rightarrow \\,\\,\\, S_{mm}=6$\n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "\n",
    "#### 适用范围及缺点\n",
    "* 使用基于进化模型的密切相关蛋白质之间的比较来推断，建立在进化单点可接受突变（PAM）模型基础上, 理论上适用于研究序列层面的进化与突变\n",
    "* 基于紧密相关蛋白质家族比对的数据\n",
    "* 使用马尔可夫模型，所以可以将高度相关蛋白的替代概率外推到远距相关蛋白的概率\n",
    "* `PAM-N`的`N`值越小，表示氨基酸变异的可能性越小；相似的序列之间比较应该选用`N`值小的矩阵，不太相似的序列之间比较应该选用`N`值大的矩阵:\n",
    "\n",
    "序列相似度 |打分矩阵\n",
    "-|-\n",
    "20% | PAM250\n",
    "40% | PAM120\n",
    "50% | PAM80\n",
    "60% | PAM60\n",
    "\n",
    "\n",
    "* PAM距离与进化时间之间没有统一的映射，因为蛋白质家族倾向于以非常不同的速率积累变化。\n",
    "* 一旦PAM1的矩阵有效地误差，那么自乘250后得到的PAM250矩阵的误差就会变得很大\n",
    "* 随着序列数据的增加，新的PAM矩阵需要构造出来\n",
    "\n",
    "---\n",
    "\n",
    "### BLOSUM (BLocks SUbstitution Matrix)\n",
    "> Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA. 1992;89(22):10915–10919. 10.1073/pnas.89.22.10915\n",
    "\n",
    "* BLOSUM matrices are derived from comparisons of blocks of sequences from the Blocks database.\n",
    "* A block is an ungapped multiple alignments of highly conserved, short regions.\n",
    "* The blocks database contains multiple alignments of conserved regions in protein families.\n",
    "\n",
    "Hangiko和Heniko（1992）提出了一种更为直接的估计目标频率的方法。他们建立了“块”的数据库，它由排列在整个蛋白质家族中相对保守的蛋白质片段组成。构造blosum（用于“block sum”）替换矩阵的$q_{ij}$是通过计算blocks数据库所隐含的所有对齐对残数而简单地导出的。通过对分块内的分块进行聚类，构造出大于二分之一的进化散度的BLUSOM矩阵，该块大于相同的百分数，并且只在簇之间但不在簇内计数对齐的残基。因此，blosum-62矩阵（大致对应于`PAM-180`）是通过对所有相同程度超过62%的片段进行第一次聚类而得到的。随着与blosum矩阵相关的数目的增加，在推导$q_{ij}$时使用了更大比例的密切相关的蛋白质片段。因此，有点令人困惑的是，高编号的blosum和低编号的pam矩阵是为紧密相关的序列量身定制的。\n",
    "\n",
    "#### Derivation a BLOSUM matrix\n",
    "The Henikoffs developed a database of \"blocks\" based on sequences with shared motifs. More than 2,000 blocks of aligned sequence segments were analyzed from more than 500 groups of related proteins. Within each block, they counted the relative frequencies of amino acids and their substitution probabilities\n",
    "\n",
    "#### Knowledge\n",
    "A BLOSUM tells us the likelihood of occurrence of each pairwise substitution, and we can use these values to score a pairwise comparison.\n",
    "\n",
    "Each scoring matrix is constructed based on how identical the ungapped multiple sequence alignments are. For example, BLOSUM62 is derived from blocks containing at most 62% identity in the ungapped sequence aligments.\n",
    "\n",
    "#### 适用范围及缺点\n",
    "* 需要有多个`Alignment`，它更容易与类似的序列对齐。\n",
    "* 不想使插入/删除的计算复杂化。\n",
    "* 想要专注于保守区域来计算得分矩阵。\n",
    "* 各种测试表明，BLUSOMU矩阵通常比原始PAM矩阵更能区分真生物关系和偶然相似性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio.SubsMat import MatrixInfo\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def plotMatrix(matrix):\n",
    "    aa_li = [i for i in 'ARNDCQEGHILKMFPSTWYV']\n",
    "    l = len(aa_li)\n",
    "    a = pd.DataFrame([[0]*l for i in aa_li])\n",
    "    for i in range(l):\n",
    "        for j in range(l):\n",
    "            try:\n",
    "                a[i][j] = matrix[(aa_li[i],aa_li[j])]\n",
    "            except KeyError:\n",
    "                a[i][j] = matrix[(aa_li[j],aa_li[i])]\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    a.columns=aa_li\n",
    "    a.index=aa_li\n",
    "    return sns.heatmap(a,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc146e9518>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotMatrix(MatrixInfo.pam250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc1b53d3c8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotMatrix(MatrixInfo.blosum62)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
